Research

Published Papers:

1. N. Ram and S. Sabach, A globally convergent inertial first-order optimization method for multidimensional scaling, Journal of Optimization Theory and Applications  202 (2024), 949--974. paper

2. E. Cohen, D. R. Luke, T. Pinta, S. Sabach and M. Teboulle, A semi-Bregman proximal alternating method for a class of nonconvex problems: local and global convergence analysis, Journal of Global Optimization  89 (2024), 33--55. paper

3. E. Gur, S. Sabach and S. Shtern, Nested alternating minimization with FISTA for non-convex and non-smooth optimization problems, Journal of Optimization Theory and Applications 199 (2023), 1130--1157. paper

4. R. Merchav and S. Sabach, Convex bi-level optimization problems with nonsmooth outer objective function, SIAM Journal on Optimization 33 (2023), 3114--3142. paper

5. E. Gur, A. Amar, and S. Sabach, Direct, fast and convergent solvers for the non-convex and non-smooth TDoA localization problem, Digital Signal Processing 139 (2023), 104074. paper

6. E. Gur, S. Sabach, and S. Shtern, Convergent nested alternating minimization algorithms for non-convex optimization problems, Mathematics of Operations Research 48 (2023), 53--77. paper

7. S. Sabach and M. Teboulle, Faster Lagrangian-based methods in convex optimization, SIAM Journal on Optimization 32  (2022), 204--227. paper‏

8. E. Cohen, S. Sabach, and M. Teboulle, Non-Euclidean proximal methods for convex-concave saddle-point problems, Journal of Applied and Numerical Optimization 3 (2021), 43--60. paper

9. D. Garber,  A. Kaplan, and S. Sabach, Improved complexities of conditional gradient-type methods with applications to robust matrix recovery problems, Mathematical Programming (Ser A.) 186 (2021), 185--208. paper

10. E. Gur, S. Sabach, and S. Shtern, Alternating minimization based first-order method for the wireless sensor network localization problem, IEEE Transactions on Signal Processing 68 (2020), 6418--6431. paper

11. T. Hazan, S. Sabach, and S. Voldman, Stochastic proximal linear method for structured non-convex problems, Optimization Methods and Software 35 (2020), 921--937. paper

12. A. Gibali, S. Sabach, and S. Voldman, Non-convex split feasibility problems: models, algorithms and theory,  Open Journal of Mathematical Optimization 1 (2020), 1--15. paper

13. M. C.  Mukkamala, P. Ochs, T. Pock, and S. Sabach, Convex-concave backtracking for inertial Bregman proximal gradient algorithms in non-convex optimization, SIAM Journal on Mathematics of Data Sciences 2 (2020), 658--682. paper

14. D. R. Luke, S. Sabach, and M. Teboulle, Optimization on spheres: models and proximal algorithms with computational performance comparison, SIAM Journal on Mathematics of Data Sciences 1 (2019), 408--445. paper

15. S. Sabach, and M. Teboulle, Lagrangian methods for composite optimization, Handbook of Numerical Analysis 20 (2019), 401--436. paper

16. J. Bolte, S. Sabach, and M. Teboulle, Nonconvex Lagrangian-based optimization: monitoring schemes and global convergence, Mathematics of Operations Research 43 (2018), 1210--1232. paper

17. S. Sabach, M. Teboulle, and S. Voldman, A smoothing alternating minimization-based algorithm for clustering with sum-min of Euclidean norms, Pure and Applied Functional Analysis 3 (2018), 653--679. paper

18. J. Bolte, S. Sabach, M. Teboulle, and Y. Vaisbourd, First order methods beyond convexity and Lipschitz gradient continuity with applications to quadratic inverse problems, SIAM Journal on Optimization 28 (2018), 2131--2151. paper

19. A. Beck, E. Pauwels, and S. Sabach, Primal and dual predicted decrease approximation methods, Mathematical Programming (Ser B.) 167 (2018), 37--73. paper

20. A. Beck, Y. C. Eldar, E. Pauwels, and S. Sabach, On Fienup methods for regularized phase retrieval, IEEE Transactions on Signal Processing 66 (2018), 982--991. paper

21. D. R. Luke, S. Sabach, M. Teboulle, and K. Zatlawey, A simple globally convergent algorithm for the single source localization problem, Journal of Global Optimization 69 (2017), 889--909. paper

22. S. Sabach and S. Shtern, A first order method for solving convex bi-level optimization problems, SIAM Journal on Optimization 27 (2017), 640--660. paper

23. T. Pock and S. Sabach, Inertial proximal alternating linearized minimization (iPALM) for nonconvex and nonsmooth problems, SIAM Journal on Imaging Sciences 9 (2016), 1756--1787. paper

24. A. Beck, S. Sabach, and M. Teboulle, An alternating semiproximal method for nonconvex regularized structured total least squares problems, SIAM Journal on Matrix Analysis and Applications 37 (2016), 1129--1150. paper

25. Y. Drori, S. Sabach, and M. Teboulle, A simple algorithm for a class of nonsmooth convex-concave saddle-point problems, Operations Research Letters 43 (2015), 209--214. paper

26. R. Hesse, D. R. Luke, S. Sabach, and M. K. Tam, Proximal heterogeneous block implicit explicit method and application to blind ptychographic diffraction imaging, SIAM Journal on Imaging Sciences 8 (2015), 426--457. paper

27. A. Beck, E. Pauwels, and S. Sabach, The cyclic block conditional gradient method for convex optimization problems, SIAM Journal on Optimization 25 (2015), 2024--2049. paper

28. A. Beck and S. Sabach, Weiszfeld’s method: old and new results, Journal of Optimization Theory and Applications 164 (2015), 1--40. paper

29. A. Beck and S. Sabach, A first order method for finding minimal norm-like solutions of convex optimization problems, Mathematical Programming (Ser. A) 147 (2014), 25--46. paper

30. J. Bolte, S. Sabach, and M. Teboulle, Proximal alternating linearized minimization for nonconvex and nosmooth problems, Mathematical Programming (Ser. A) 146 (2014), 459--494. paper

31. V. Martin-Marquez, S. Reich, and S. Sabach, Bregman strongly nonexpansive operators in reflexive Banach spaces, Journal of Mathematical Analysis and Applications 400 (2013), 597--614. paper

32. V. Martin-Marquez, S. Reich, and S. Sabach, Iterative methods for approximating fixed points of Bregman nonexpansive operators, Discrete and Continuous Dynamical Systems 6 (2013), 1043--1063. paper

33. R. S. Burachik, C. Y. Kaya, and S. Sabach, A generalized univariate Newton method motivated by proximal regularization, Journal of Optimization Theory and Applications 155 (2012), 923--940. paper

34. A. Beck, and S. Sabach, An improved ellipsoid method for solving convex differentiable optimization problems, Operations Research Letters 40 (2012), 541--545. paper

35. V. Martin-Marquez, S. Reich, and S. Sabach, Right Bregman nonexpansive operators in Banach spaces, Nonlinear Analysis 75 (2012), 5448--5465. paper

36. Y. Censor, A. Gibali, S. Reich, and S. Sabach, The common variational inequality point problem, Set-Valued and Variational Analysis 20 (2012), 229--247. paper

37. S. Reich and S. Sabach, A projection method for solving nonlinear problems in reflexive Banach spaces, Journal of Fixed Point Theory and Applications 9 (2011), 101--116. paper

38. J. M. Borwein, S. Reich, and S. Sabach, Characterization of Bregman firmly nonexpansive operators using new type of monotonicity, Journal of Nonlinear and Convex Analysis 12 (2011), 161--183. paper

39. S. Sabach, Products of finitely many resolvents of maximal monotone mappings in reflexive Banach spaces, SIAM Journal on Optimization 21 (2011), 1289--1308. paper

40. G. Kassay, S. Reich, and S. Sabach, Iterative methods for solving systems of variational inequalities in reflexive Banach spaces, SIAM Journal on Optimization 21 (2011), 1319--1344. paper

41. S. Reich and S. Sabach, Two strong convergence theorems for Bregman strongly nonexpansive operators in reflexive Banach Spaces, Nonlinear Analysis 73 (2010), 122--135. paper

42. S. Reich and S. Sabach, Two strong convergence theorems for a proximal method in reflexive Banach spaces, Numerical Functional Analysis and Optimization 31 (2010), 22--44. paper

43. D. Butnariu, S. Reich, and S. Sabach, A strong convergence theorem for resolvents of monotone operators, Journal of Convex Analysis 17 (2010), 991--1006. paper

44. S. Reich and S. Sabach, A strong convergence theorem for a proximal-type algorithm in reflexive Banach spaces, Journal of Nonlinear and Convex Analysis 10 (2009), 471--485. paper

Refereed papers in conference proceedings:

45. K. Asadi, Y. Liu, S. Sabach, M. Yin, and R. Fakoor, Learning the target network in function space, Proceedings of the 41st International Conference on Machine Learning  PMLR 235 (2024), 1902--1923. paper

46. K. Ozkara, C. Karakus, P. Raman, M. Hong, S. Sabach, B. Kveton, and V. Cevher, MADA: Meta-adaptive optimizers through hyper-gradient descent, Proceedings of the 41st International Conference on Machine Learning PMLR 235 (2024), 38983--39008. paper

47. R. Jiang, P. Raman, S. Sabach, A. Mokhtari, M. Hong, and V. Cevher, Krylov cubic regularized Newton: A subspace second-order method with dimension-free convergence rate, Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS) PMLR 238 (2024), 4411--4419. paper

48. Z. Liu, J. Zhang, K. Asadi, Y. Liu, D. Zhao, S. Sabach, and R. Fakoor, TAIL, Task-specific adapters for imitation learning with large pretrained models, 12th International Conference on Learning Representations (ICLR 2024). paper

49. K. Asadi, S. Sabach, Y. Liu, O. Gottesman, and R. Fakoor, TD convergence: An optimization perspective, Advances in Neural Information Processing Systems (NeurIPS) 36 (2023) 49169--49186. paper

50. K. Asadi, R. Fakoor, and S. Sabach, Resetting the optimizer in deep RL: An empirical study, Advances in Neural Information Processing Systems (NeurIPS) 36 (2023),  72284--72324. paper

51. D. Garber, T. Livney, and S. Sabach, Faster projection-free augmented Lagrangian methods via weak proximal oracle, Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS) PMLR 206 (2023), 7213--7238. paper

52. E. Markovskiy, F. Raiber, S. Sabach, and O. Kurland, From cluster ranking to document ranking, Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (2022), 2137--2141. paper

Refereed papers in proceedings:

53. S. Reich and S. Sabach, Three strong convergence theorems for iterative methods for solving equilibrium problems in reflexive Banach spaces, in ``Optimisation Theory and Related Topics”, Contemporary Mathematics, vol. 568, Amer. Math. Soc., Providence, RI, 2012, 225--240. paper

54. V. Martin-Marquez, S. Reich, and S. Sabach, Existence and approximation of fixed points of right Bregman nonexpansive operators, in ``Computational and Analytical Mathematics”, Springer, New York, 2012, 501--520. paper

55. S. Reich and S. Sabach, Existence and approximation of fixed points of Bregman firmly nonexpansive mappings in reflexive Banach spaces, in ``Fixed-Point Algorithms for Inverse Problems in Science and Engineering”, Springer, New York, 2010, 299--314. paper

56. D. Butnariu, E. Resmerita, and S. Sabach, A Mosco stability theorem for generalized proximal mappings, in ``Nonlinear Analysis and Optimization I”, Contemporary Mathematics, vol. 513, Amer. Math. Soc., Providence, RI, 2010, 99--110. paper

Preprint papers:

1. R. Merchav, S. Sabach, and M. Teboulle, A fast algorithm for convex composite bi-level optimization. Uner review. Arxiv‏

2. E. Gur and S. Sabach, Network localization and multi-dimensional scaling: escaping saddles and a local optimality condition. Uner review.

3. E. Gur, A. Amar, and S. Sabach, A dual-based first-order algorithm for ToA asynchronous localization and synchronization. Uner review. 

4. E. Gur and S. Sabach, Fast and convergent method for large-scale sensor network localization with anchor uncertainty. Uner review. 

5. K. Antonakopoulos, S. Sabach, L. Viano, M. Hong, and V. Cevher, Adaptive bilevel optimization. Uner review.

Google Scholar page: 

https://scholar.google.com/citations?hl=iw&user=42D12TkAAAAJ